FigureS3_bleo

Load Data

dataset <- read.delim("raw_data/FigureS3_bleo.txt", stringsAsFactors = FALSE)

dataset$genotype <- gsub(" ", "", dataset$genotype )
dataset$genotype <- factor(dataset$genotype)
dataset$Experiment <- factor(rep(paste0("exp", 1:(length(dataset$genotype)/length(levels(dataset$genotype)))), each=length(unique(dataset$genotype))))

dataset$siRNA <-  factor(gsub(".*[T,O]\\+","",dataset$genotype))
dataset$genotype <-  factor(gsub("\\+.*","",dataset$genotype))

dataset$UID <- factor(paste(dataset$Experiment, dataset$genotype, dataset$siRNA))
dataset$GSID <- factor(paste(dataset$genotype, dataset$siRNA))

# wide format
kable(dataset, row.names = F)

genotype	NT	bleomycin_5uM	bleomycin_10uM	bleomycin_50uM	Experiment	siRNA	UID	GSID
WT	457	286	170	51	exp1	siCtrl	exp1 WT siCtrl	WT siCtrl
WT	199	58	55	14	exp1	siPARP1	exp1 WT siPARP1	WT siPARP1
ALC1KO	516	85	57	27	exp1	siCtrl	exp1 ALC1KO siCtrl	ALC1KO siCtrl
ALC1KO	145	41	24	4	exp1	siPARP1	exp1 ALC1KO siPARP1	ALC1KO siPARP1
WT	1745	1146	656	222	exp2	siCtrl	exp2 WT siCtrl	WT siCtrl
WT	1356	368	298	33	exp2	siPARP1	exp2 WT siPARP1	WT siPARP1
ALC1KO	2384	460	361	65	exp2	siCtrl	exp2 ALC1KO siCtrl	ALC1KO siCtrl
ALC1KO	2208	864	551	45	exp2	siPARP1	exp2 ALC1KO siPARP1	ALC1KO siPARP1
WT	1912	1436	722	342	exp3	siCtrl	exp3 WT siCtrl	WT siCtrl
WT	1896	422	276	45	exp3	siPARP1	exp3 WT siPARP1	WT siPARP1
ALC1KO	3403	1988	389	58	exp3	siCtrl	exp3 ALC1KO siCtrl	ALC1KO siCtrl
ALC1KO	1808	551	268	77	exp3	siPARP1	exp3 ALC1KO siPARP1	ALC1KO siPARP1

library(reshape2)
# reshape to long format
dataset <- melt(dataset, variable.name = "Treatment", value.name = "Counts")

dataset$genotype <- relevel(dataset$genotype, ref = "WT")
dataset$siRNA <- relevel(dataset$siRNA, ref = "siCtrl")
dataset$UID <- relevel(dataset$UID, ref = "exp1 WT siCtrl")

dataset$Bleomycin <- gsub("NT","1",dataset$Treatment)
dataset$Bleomycin <- gsub("bleomycin_|uM","",dataset$Bleomycin)
dataset$Bleomycin <- log10(as.integer(dataset$Bleomycin))




dataset$Offset <- NA
for(uid in levels(dataset$UID)){
        dataset$Offset[dataset$UID == uid] <- mean(dataset$Counts[dataset$UID == uid])
}

dataset$NormCounts <- dataset$Counts / dataset$Offset



dataset$Offset2 <- NA
for(gsid in levels(dataset$GSID)){
        dataset$Offset2[dataset$GSID == gsid] <- mean(dataset$NormCounts[dataset$GSID == gsid & dataset$Bleomycin == 0])
}

dataset$NormCounts2 <- dataset$NormCounts / dataset$Offset2



# long format
kable(dataset, row.names = F)

genotype	Experiment	siRNA	UID	GSID	Treatment	Counts	Bleomycin	Offset	NormCounts	Offset2	NormCounts2
WT	exp1	siCtrl	exp1 WT siCtrl	WT siCtrl	NT	457	0.00000	241.00	1.8962656	1.827223	1.0377853
WT	exp1	siPARP1	exp1 WT siPARP1	WT siPARP1	NT	199	0.00000	81.50	2.4417178	2.651650	0.9208296
ALC1KO	exp1	siCtrl	exp1 ALC1KO siCtrl	ALC1KO siCtrl	NT	516	0.00000	171.25	3.0131387	2.753656	1.0942322
ALC1KO	exp1	siPARP1	exp1 ALC1KO siPARP1	ALC1KO siPARP1	NT	145	0.00000	53.50	2.7102804	2.597563	1.0433936
WT	exp2	siCtrl	exp2 WT siCtrl	WT siCtrl	NT	1745	0.00000	942.25	1.8519501	1.827223	1.0135325
WT	exp2	siPARP1	exp2 WT siPARP1	WT siPARP1	NT	1356	0.00000	513.75	2.6394161	2.651650	0.9953863
ALC1KO	exp2	siCtrl	exp2 ALC1KO siCtrl	ALC1KO siCtrl	NT	2384	0.00000	817.50	2.9162080	2.753656	1.0590314
ALC1KO	exp2	siPARP1	exp2 ALC1KO siPARP1	ALC1KO siPARP1	NT	2208	0.00000	917.00	2.4078517	2.597563	0.9269657
WT	exp3	siCtrl	exp3 WT siCtrl	WT siCtrl	NT	1912	0.00000	1103.00	1.7334542	1.827223	0.9486822
WT	exp3	siPARP1	exp3 WT siPARP1	WT siPARP1	NT	1896	0.00000	659.75	2.8738158	2.651650	1.0837840
ALC1KO	exp3	siCtrl	exp3 ALC1KO siCtrl	ALC1KO siCtrl	NT	3403	0.00000	1459.50	2.3316204	2.753656	0.8467364
ALC1KO	exp3	siPARP1	exp3 ALC1KO siPARP1	ALC1KO siPARP1	NT	1808	0.00000	676.00	2.6745562	2.597563	1.0296407
WT	exp1	siCtrl	exp1 WT siCtrl	WT siCtrl	bleomycin_5uM	286	0.69897	241.00	1.1867220	1.827223	0.6494674
WT	exp1	siPARP1	exp1 WT siPARP1	WT siPARP1	bleomycin_5uM	58	0.69897	81.50	0.7116564	2.651650	0.2683825
ALC1KO	exp1	siCtrl	exp1 ALC1KO siCtrl	ALC1KO siCtrl	bleomycin_5uM	85	0.69897	171.25	0.4963504	2.753656	0.1802514
ALC1KO	exp1	siPARP1	exp1 ALC1KO siPARP1	ALC1KO siPARP1	bleomycin_5uM	41	0.69897	53.50	0.7663551	2.597563	0.2950285
WT	exp2	siCtrl	exp2 WT siCtrl	WT siCtrl	bleomycin_5uM	1146	0.69897	942.25	1.2162377	1.827223	0.6656207
WT	exp2	siPARP1	exp2 WT siPARP1	WT siPARP1	bleomycin_5uM	368	0.69897	513.75	0.7163017	2.651650	0.2701343
ALC1KO	exp2	siCtrl	exp2 ALC1KO siCtrl	ALC1KO siCtrl	bleomycin_5uM	460	0.69897	817.50	0.5626911	2.753656	0.2043433
ALC1KO	exp2	siPARP1	exp2 ALC1KO siPARP1	ALC1KO siPARP1	bleomycin_5uM	864	0.69897	917.00	0.9422028	2.597563	0.3627257
WT	exp3	siCtrl	exp3 WT siCtrl	WT siCtrl	bleomycin_5uM	1436	0.69897	1103.00	1.3019039	1.827223	0.7125040
WT	exp3	siPARP1	exp3 WT siPARP1	WT siPARP1	bleomycin_5uM	422	0.69897	659.75	0.6396362	2.651650	0.2412220
ALC1KO	exp3	siCtrl	exp3 ALC1KO siCtrl	ALC1KO siCtrl	bleomycin_5uM	1988	0.69897	1459.50	1.3621103	2.753656	0.4946553
ALC1KO	exp3	siPARP1	exp3 ALC1KO siPARP1	ALC1KO siPARP1	bleomycin_5uM	551	0.69897	676.00	0.8150888	2.597563	0.3137898
WT	exp1	siCtrl	exp1 WT siCtrl	WT siCtrl	bleomycin_10uM	170	1.00000	241.00	0.7053942	1.827223	0.3860471
WT	exp1	siPARP1	exp1 WT siPARP1	WT siPARP1	bleomycin_10uM	55	1.00000	81.50	0.6748466	2.651650	0.2545007
ALC1KO	exp1	siCtrl	exp1 ALC1KO siCtrl	ALC1KO siCtrl	bleomycin_10uM	57	1.00000	171.25	0.3328467	2.753656	0.1208745
ALC1KO	exp1	siPARP1	exp1 ALC1KO siPARP1	ALC1KO siPARP1	bleomycin_10uM	24	1.00000	53.50	0.4485981	2.597563	0.1726996
WT	exp2	siCtrl	exp2 WT siCtrl	WT siCtrl	bleomycin_10uM	656	1.00000	942.25	0.6962059	1.827223	0.3810185
WT	exp2	siPARP1	exp2 WT siPARP1	WT siPARP1	bleomycin_10uM	298	1.00000	513.75	0.5800487	2.651650	0.2187501
ALC1KO	exp2	siCtrl	exp2 ALC1KO siCtrl	ALC1KO siCtrl	bleomycin_10uM	361	1.00000	817.50	0.4415902	2.753656	0.1603651
ALC1KO	exp2	siPARP1	exp2 ALC1KO siPARP1	ALC1KO siPARP1	bleomycin_10uM	551	1.00000	917.00	0.6008724	2.597563	0.2313216
WT	exp3	siCtrl	exp3 WT siCtrl	WT siCtrl	bleomycin_10uM	722	1.00000	1103.00	0.6545784	1.827223	0.3582367
WT	exp3	siPARP1	exp3 WT siPARP1	WT siPARP1	bleomycin_10uM	276	1.00000	659.75	0.4183403	2.651650	0.1577660
ALC1KO	exp3	siCtrl	exp3 ALC1KO siCtrl	ALC1KO siCtrl	bleomycin_10uM	389	1.00000	1459.50	0.2665296	2.753656	0.0967912
ALC1KO	exp3	siPARP1	exp3 ALC1KO siPARP1	ALC1KO siPARP1	bleomycin_10uM	268	1.00000	676.00	0.3964497	2.597563	0.1526237
WT	exp1	siCtrl	exp1 WT siCtrl	WT siCtrl	bleomycin_50uM	51	1.69897	241.00	0.2116183	1.827223	0.1158141
WT	exp1	siPARP1	exp1 WT siPARP1	WT siPARP1	bleomycin_50uM	14	1.69897	81.50	0.1717791	2.651650	0.0647820
ALC1KO	exp1	siCtrl	exp1 ALC1KO siCtrl	ALC1KO siCtrl	bleomycin_50uM	27	1.69897	171.25	0.1576642	2.753656	0.0572563
ALC1KO	exp1	siPARP1	exp1 ALC1KO siPARP1	ALC1KO siPARP1	bleomycin_50uM	4	1.69897	53.50	0.0747664	2.597563	0.0287833
WT	exp2	siCtrl	exp2 WT siCtrl	WT siCtrl	bleomycin_50uM	222	1.69897	942.25	0.2356063	1.827223	0.1289422
WT	exp2	siPARP1	exp2 WT siPARP1	WT siPARP1	bleomycin_50uM	33	1.69897	513.75	0.0642336	2.651650	0.0242240
ALC1KO	exp2	siCtrl	exp2 ALC1KO siCtrl	ALC1KO siCtrl	bleomycin_50uM	65	1.69897	817.50	0.0795107	2.753656	0.0288746
ALC1KO	exp2	siPARP1	exp2 ALC1KO siPARP1	ALC1KO siPARP1	bleomycin_50uM	45	1.69897	917.00	0.0490731	2.597563	0.0188920
WT	exp3	siCtrl	exp3 WT siCtrl	WT siCtrl	bleomycin_50uM	342	1.69897	1103.00	0.3100635	1.827223	0.1696911
WT	exp3	siPARP1	exp3 WT siPARP1	WT siPARP1	bleomycin_50uM	45	1.69897	659.75	0.0682077	2.651650	0.0257227
ALC1KO	exp3	siCtrl	exp3 ALC1KO siCtrl	ALC1KO siCtrl	bleomycin_50uM	58	1.69897	1459.50	0.0397396	2.753656	0.0144316
ALC1KO	exp3	siPARP1	exp3 ALC1KO siPARP1	ALC1KO siPARP1	bleomycin_50uM	77	1.69897	676.00	0.1139053	2.597563	0.0438508

Plot Data

library(ggplot2)

# raw data
ggplot(dataset, aes(x=Bleomycin, y=Counts)) + 
        theme_bw() +
        theme(panel.grid=element_blank(), text = element_text(size=14)) +
        geom_smooth(method=lm, formula = y ~ poly(x,2), se=FALSE, aes(colour=siRNA)) +
        geom_point(aes(colour=siRNA, shape=Experiment), size=2) +        
        facet_grid(. ~ genotype) +
        xlab(label = "Bleomycin (log10 µM)") +
        scale_shape_manual(values=15:20) +
        scale_color_manual(values=c("#000000","#FF0000"))

# NormCounts Linear
ggplot(dataset, aes(x=Bleomycin, y=NormCounts, color=siRNA)) + 
        theme_bw() +
        theme(panel.grid=element_blank(), text = element_text(size=14)) +
        geom_point(aes(colour=siRNA), size=2) +        
        geom_smooth(method=lm, formula = y ~ x, se=FALSE) +
        facet_grid(. ~ genotype) +
        xlab(label = "Bleomycin (log10 µM)") +
        scale_color_manual(values=c("#000000","#FF0000"))

# NormCounts2 Linear
ggplot(dataset, aes(x=Bleomycin, y=NormCounts2, color=siRNA)) + 
        theme_bw() +
        theme(panel.grid=element_blank(), text = element_text(size=14)) +
        geom_point(aes(colour=siRNA), size=2) +        
        geom_smooth(method=lm, formula = y ~ x, se=FALSE) +
        facet_grid(. ~ genotype) +
        xlab(label = "Bleomycin (log10 µM)") +
        scale_color_manual(values=c("#000000","#FF0000"))

# NormCounts Quadratic
ggplot(dataset, aes(x=Bleomycin, y=NormCounts, color=siRNA)) + 
        theme_bw() +
        theme(panel.grid=element_blank(), text = element_text(size=14)) +
        geom_point(aes(colour=siRNA), size=2) +        
        geom_smooth(method=lm, formula = y ~ poly(x,2), se=FALSE) +
        facet_grid(. ~ genotype) +
        xlab(label = "Bleomycin (log10 µM)")+
        scale_color_manual(values=c("#000000","#FF0000"))

# NormCounts2 Quadratic
ggplot(dataset, aes(x=Bleomycin, y=NormCounts2, color=siRNA)) + 
        theme_bw() +
        theme(panel.grid=element_blank(), text = element_text(size=14)) +
        geom_point(aes(colour=siRNA), size=2) +        
        geom_smooth(method=lm, formula = y ~ poly(x,2), se=FALSE) +
        facet_grid(. ~ genotype) +
        xlab(label = "Bleomycin (log10 µM)") +
        scale_color_manual(values=c("#000000","#FF0000"))

# NormCounts Cubic
ggplot(dataset, aes(x=Bleomycin, y=NormCounts, color=siRNA)) + 
        theme_bw() +
        theme(panel.grid=element_blank(), text = element_text(size=14)) +
        geom_point(aes(colour=siRNA), size=2) +        
        geom_smooth(method=lm, formula = y ~ poly(x,3), se=FALSE) +
        facet_grid(. ~ genotype) +
        xlab(label = "Bleomycin (log10 µM)")+
        scale_color_manual(values=c("#000000","#FF0000"))

# NormCounts2 Cubic
ggplot(dataset, aes(x=Bleomycin, y=NormCounts2, color=siRNA)) + 
        theme_bw() +
        theme(panel.grid=element_blank(), text = element_text(size=14)) +
        geom_point(aes(colour=siRNA), size=2) +        
        geom_smooth(method=lm, formula = y ~ poly(x,3), se=FALSE) +
        facet_grid(. ~ genotype) +
        xlab(label = "Bleomycin (log10 µM)") +
        scale_color_manual(values=c("#000000","#FF0000"))

library(Cairo)


cairo_pdf("FigureS3_bleo.pdf", width = 5, height = 4, family = "Arial")

ggplot(dataset, aes(x=Bleomycin, y=NormCounts2)) + 
        theme_bw() +
        theme(panel.grid.major=element_blank(), panel.grid.minor=element_blank(), 
              axis.line = element_line(colour = "black"), text = element_text(size=14),
              panel.border = element_blank(), panel.background = element_blank()) +
        geom_point(aes(colour = siRNA, shape = genotype), size=1.75) +
        geom_smooth(method=lm, formula = y ~ poly(x,2), se=TRUE, 
                    aes(group = GSID,colour = siRNA, linetype = genotype), fill='#DDDDDD', size=0.5) +
        #facet_grid(. ~ genotype) +
        xlab(label = "Bleomycin (log10 µM)") +
        ylab(label = "Normalized Counts") +
        scale_color_manual(values=c("#000000","#FF0000")) +
        guides(linetype = guide_legend(override.aes= list(color = "#555555"))) 

dev.off()

## quartz_off_screen 
##                 2

Models

library(MASS)
library(DHARMa)
library(lme4)
library(lmerTest)
library(bbmle)

Linear formula

fit1 <- lm(Counts ~ Experiment + Bleomycin*siRNA*genotype, data = dataset)
print(summary(fit1))

## 
## Call:
## lm(formula = Counts ~ Experiment + Bleomycin * siRNA * genotype, 
##     data = dataset)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -844.27 -280.80  -46.55  227.69 1204.98 
## 
## Coefficients:
##                                       Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                             865.77     253.57   3.414 0.001533 ** 
## Experimentexp2                          660.81     167.46   3.946 0.000331 ***
## Experimentexp3                          837.75     167.46   5.003 1.32e-05 ***
## Bleomycin                              -710.09     224.14  -3.168 0.003025 ** 
## siRNAsiPARP1                           -397.90     331.51  -1.200 0.237463    
## genotypeALC1KO                          494.49     331.51   1.492 0.144044    
## Bleomycin:siRNAsiPARP1                   63.74     316.98   0.201 0.841702    
## Bleomycin:genotypeALC1KO               -518.54     316.98  -1.636 0.110119    
## siRNAsiPARP1:genotypeALC1KO            -243.48     468.82  -0.519 0.606537    
## Bleomycin:siRNAsiPARP1:genotypeALC1KO   376.67     448.27   0.840 0.406014    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 473.6 on 38 degrees of freedom
## Multiple R-squared:  0.7123, Adjusted R-squared:  0.6442 
## F-statistic: 10.46 on 9 and 38 DF,  p-value: 6.495e-08

cat("AIC: ", AIC(fit1))

## AIC:  738.4095

simres <- simulateResiduals(fittedModel = fit1)
plot(simres)

fit2 <- lm(NormCounts ~ Bleomycin*siRNA*genotype, data = dataset)
print(summary(fit2))

## 
## Call:
## lm(formula = NormCounts ~ Bleomycin * siRNA * genotype, data = dataset)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -0.7392 -0.3245  0.0422  0.3185  0.6835 
## 
## Coefficients:
##                                       Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                             1.8106     0.2000   9.055 3.12e-11 ***
## Bleomycin                              -0.9543     0.1912  -4.991 1.22e-05 ***
## siRNAsiPARP1                            0.4370     0.2828   1.545   0.1302    
## genotypeALC1KO                          0.5191     0.2828   1.836   0.0739 .  
## Bleomycin:siRNAsiPARP1                 -0.5144     0.2704  -1.902   0.0643 .  
## Bleomycin:genotypeALC1KO               -0.6110     0.2704  -2.260   0.0294 *  
## siRNAsiPARP1:genotypeALC1KO            -0.5150     0.3999  -1.288   0.2053    
## Bleomycin:siRNAsiPARP1:genotypeALC1KO   0.6062     0.3824   1.585   0.1208    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.404 on 40 degrees of freedom
## Multiple R-squared:  0.8402, Adjusted R-squared:  0.8123 
## F-statistic: 30.05 on 7 and 40 DF,  p-value: 5.166e-14

cat("AIC: ", AIC(fit2))

## AIC:  58.46559

simres <- simulateResiduals(fittedModel = fit2)
plot(simres)

fit3 <- lm(NormCounts2 ~ Bleomycin*siRNA*genotype, data = dataset)
print(summary(fit3))

## 
## Call:
## lm(formula = NormCounts2 ~ Bleomycin * siRNA * genotype, data = dataset)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -0.2685 -0.1196  0.0231  0.1207  0.2482 
## 
## Coefficients:
##                                       Estimate Std. Error t value Pr(>|t|)    
## (Intercept)                            0.99092    0.07553  13.120 4.47e-16 ***
## Bleomycin                             -0.52225    0.07222  -7.232 8.94e-09 ***
## siRNAsiPARP1                          -0.14330    0.10681  -1.342    0.187    
## genotypeALC1KO                        -0.14489    0.10681  -1.356    0.183    
## Bleomycin:siRNAsiPARP1                -0.03162    0.10213  -0.310    0.759    
## Bleomycin:genotypeALC1KO              -0.04619    0.10213  -0.452    0.654    
## siRNAsiPARP1:genotypeALC1KO            0.16411    0.15106   1.086    0.284    
## Bleomycin:siRNAsiPARP1:genotypeALC1KO  0.03281    0.14444   0.227    0.821    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.1526 on 40 degrees of freedom
## Multiple R-squared:  0.8603, Adjusted R-squared:  0.8359 
## F-statistic: 35.19 on 7 and 40 DF,  p-value: 3.715e-15

cat("AIC: ", AIC(fit3))

## AIC:  -34.99918

simres <- simulateResiduals(fittedModel = fit3)
plot(simres)

fit4 <- lmer(Counts ~ Bleomycin*siRNA*genotype + (1|UID), data = dataset)
print(summary(fit4))

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Counts ~ Bleomycin * siRNA * genotype + (1 | UID)
##    Data: dataset
## 
## REML criterion at convergence: 634.7
## 
## Scaled residuals: 
##     Min      1Q  Median      3Q     Max 
## -1.7888 -0.4565 -0.1777  0.6047  2.2043 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  UID      (Intercept) 170269   412.6   
##  Residual             232885   482.6   
## Number of obs: 48, groups:  UID, 12
## 
## Fixed effects:
##                                       Estimate Std. Error      df t value
## (Intercept)                            1365.29     337.34   16.83   4.047
## Bleomycin                              -710.09     228.36   32.00  -3.109
## siRNAsiPARP1                           -397.90     477.07   16.83  -0.834
## genotypeALC1KO                          494.49     477.07   16.83   1.037
## Bleomycin:siRNAsiPARP1                   63.74     322.96   32.00   0.197
## Bleomycin:genotypeALC1KO               -518.54     322.96   32.00  -1.606
## siRNAsiPARP1:genotypeALC1KO            -243.48     674.67   16.83  -0.361
## Bleomycin:siRNAsiPARP1:genotypeALC1KO   376.67     456.73   32.00   0.825
##                                       Pr(>|t|)    
## (Intercept)                           0.000852 ***
## Bleomycin                             0.003920 ** 
## siRNAsiPARP1                          0.415934    
## genotypeALC1KO                        0.314618    
## Bleomycin:siRNAsiPARP1                0.844789    
## Bleomycin:genotypeALC1KO              0.118183    
## siRNAsiPARP1:genotypeALC1KO           0.722679    
## Bleomycin:siRNAsiPARP1:genotypeALC1KO 0.415639    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##             (Intr) Blmycn sRNAsPARP1 gALC1K Bl:RNAPARP1 B:ALC1 sRNAPARP1:
## Bleomycin   -0.575                                                       
## siRNAsPARP1 -0.707  0.407                                                
## gntypALC1KO -0.707  0.407  0.500                                         
## Bl:RNAPARP1  0.407 -0.707 -0.575     -0.288                              
## Blmy:ALC1KO  0.407 -0.707 -0.288     -0.575  0.500                       
## sRNAPARP1:A  0.500 -0.288 -0.707     -0.707  0.407       0.407           
## B:RNAPARP1: -0.288  0.500  0.407      0.407 -0.707      -0.707 -0.575

cat("AIC: ", AIC(fit4))

## AIC:  654.7104

simres <- simulateResiduals(fittedModel = fit4)
plot(simres)

Quadratic formula

fit5 <- lm(Counts ~ Experiment + poly(Bleomycin,2)*siRNA*genotype, data = dataset)
print(summary(fit5))

## 
## Call:
## lm(formula = Counts ~ Experiment + poly(Bleomycin, 2) * siRNA * 
##     genotype, data = dataset)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1103.68  -248.93   -34.77   204.19   945.57 
## 
## Coefficients:
##                                                 Estimate Std. Error t value
## (Intercept)                                        262.6      160.7   1.634
## Experimentexp2                                     660.8      160.7   4.112
## Experimentexp3                                     837.8      160.7   5.213
## poly(Bleomycin, 2)1                              -3001.1      909.1  -3.301
## poly(Bleomycin, 2)2                                180.7      909.1   0.199
## siRNAsiPARP1                                      -343.8      185.6  -1.852
## genotypeALC1KO                                      54.0      185.6   0.291
## poly(Bleomycin, 2)1:siRNAsiPARP1                   269.4     1285.6   0.210
## poly(Bleomycin, 2)2:siRNAsiPARP1                  1012.1     1285.6   0.787
## poly(Bleomycin, 2)1:genotypeALC1KO               -2191.6     1285.6  -1.705
## poly(Bleomycin, 2)2:genotypeALC1KO                1616.6     1285.6   1.257
## siRNAsiPARP1:genotypeALC1KO                         76.5      262.4   0.292
## poly(Bleomycin, 2)1:siRNAsiPARP1:genotypeALC1KO   1592.0     1818.1   0.876
## poly(Bleomycin, 2)2:siRNAsiPARP1:genotypeALC1KO  -1661.6     1818.1  -0.914
##                                                 Pr(>|t|)    
## (Intercept)                                     0.111515    
## Experimentexp2                                  0.000235 ***
## Experimentexp3                                  9.09e-06 ***
## poly(Bleomycin, 2)1                             0.002268 ** 
## poly(Bleomycin, 2)2                             0.843612    
## siRNAsiPARP1                                    0.072653 .  
## genotypeALC1KO                                  0.772812    
## poly(Bleomycin, 2)1:siRNAsiPARP1                0.835271    
## poly(Bleomycin, 2)2:siRNAsiPARP1                0.436588    
## poly(Bleomycin, 2)1:genotypeALC1KO              0.097371 .  
## poly(Bleomycin, 2)2:genotypeALC1KO              0.217160    
## siRNAsiPARP1:genotypeALC1KO                     0.772429    
## poly(Bleomycin, 2)1:siRNAsiPARP1:genotypeALC1KO 0.387384    
## poly(Bleomycin, 2)2:siRNAsiPARP1:genotypeALC1KO 0.367195    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 454.5 on 34 degrees of freedom
## Multiple R-squared:  0.763,  Adjusted R-squared:  0.6724 
## F-statistic: 8.419 on 13 and 34 DF,  p-value: 3.064e-07

cat("AIC: ", AIC(fit5))

## AIC:  737.115

simres <- simulateResiduals(fittedModel = fit5)
plot(simres)

fit6 <- lm(NormCounts ~ poly(Bleomycin,2)*siRNA*genotype, data = dataset)
print(summary(fit6))

## 
## Call:
## lm(formula = NormCounts ~ poly(Bleomycin, 2) * siRNA * genotype, 
##     data = dataset)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.42105 -0.11261 -0.00354  0.08330  0.54949 
## 
## Coefficients:
##                                                   Estimate Std. Error t value
## (Intercept)                                      1.000e+00  5.461e-02  18.313
## poly(Bleomycin, 2)1                             -4.033e+00  3.783e-01 -10.660
## poly(Bleomycin, 2)2                              2.759e-01  3.783e-01   0.729
## siRNAsiPARP1                                    -1.132e-16  7.723e-02   0.000
## genotypeALC1KO                                  -3.754e-16  7.723e-02   0.000
## poly(Bleomycin, 2)1:siRNAsiPARP1                -2.174e+00  5.350e-01  -4.063
## poly(Bleomycin, 2)2:siRNAsiPARP1                 2.333e+00  5.350e-01   4.360
## poly(Bleomycin, 2)1:genotypeALC1KO              -2.582e+00  5.350e-01  -4.827
## poly(Bleomycin, 2)2:genotypeALC1KO               2.655e+00  5.350e-01   4.961
## siRNAsiPARP1:genotypeALC1KO                      2.671e-16  1.092e-01   0.000
## poly(Bleomycin, 2)1:siRNAsiPARP1:genotypeALC1KO  2.562e+00  7.567e-01   3.386
## poly(Bleomycin, 2)2:siRNAsiPARP1:genotypeALC1KO -2.919e+00  7.567e-01  -3.857
##                                                 Pr(>|t|)    
## (Intercept)                                      < 2e-16 ***
## poly(Bleomycin, 2)1                             1.09e-12 ***
## poly(Bleomycin, 2)2                             0.470521    
## siRNAsiPARP1                                    1.000000    
## genotypeALC1KO                                  1.000000    
## poly(Bleomycin, 2)1:siRNAsiPARP1                0.000251 ***
## poly(Bleomycin, 2)2:siRNAsiPARP1                0.000104 ***
## poly(Bleomycin, 2)1:genotypeALC1KO              2.55e-05 ***
## poly(Bleomycin, 2)2:genotypeALC1KO              1.69e-05 ***
## siRNAsiPARP1:genotypeALC1KO                     1.000000    
## poly(Bleomycin, 2)1:siRNAsiPARP1:genotypeALC1KO 0.001727 ** 
## poly(Bleomycin, 2)2:siRNAsiPARP1:genotypeALC1KO 0.000456 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.1892 on 36 degrees of freedom
## Multiple R-squared:  0.9685, Adjusted R-squared:  0.9588 
## F-statistic: 100.5 on 11 and 36 DF,  p-value: < 2.2e-16

cat("AIC: ", AIC(fit6))

## AIC:  -11.44408

simres <- simulateResiduals(fittedModel = fit6)
plot(simres)

fit7 <- lm(NormCounts2 ~ poly(Bleomycin,2)*siRNA*genotype, data = dataset)
print(summary(fit7))

## 
## Call:
## lm(formula = NormCounts2 ~ poly(Bleomycin, 2) * siRNA * genotype, 
##     data = dataset)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.152905 -0.045876 -0.001149  0.038348  0.199550 
## 
## Coefficients:
##                                                 Estimate Std. Error t value
## (Intercept)                                      0.54728    0.02136  25.618
## poly(Bleomycin, 2)1                             -2.20725    0.14801 -14.913
## poly(Bleomycin, 2)2                              0.15101    0.14801   1.020
## siRNAsiPARP1                                    -0.17015    0.03021  -5.632
## genotypeALC1KO                                  -0.18412    0.03021  -6.094
## poly(Bleomycin, 2)1:siRNAsiPARP1                -0.13362    0.20931  -0.638
## poly(Bleomycin, 2)2:siRNAsiPARP1                 0.83278    0.20931   3.979
## poly(Bleomycin, 2)1:genotypeALC1KO              -0.19522    0.20931  -0.933
## poly(Bleomycin, 2)2:genotypeALC1KO               0.91321    0.20931   4.363
## siRNAsiPARP1:genotypeALC1KO                      0.19198    0.04273   4.493
## poly(Bleomycin, 2)1:siRNAsiPARP1:genotypeALC1KO  0.13865    0.29601   0.468
## poly(Bleomycin, 2)2:siRNAsiPARP1:genotypeALC1KO -0.99441    0.29601  -3.359
##                                                 Pr(>|t|)    
## (Intercept)                                      < 2e-16 ***
## poly(Bleomycin, 2)1                              < 2e-16 ***
## poly(Bleomycin, 2)2                             0.314405    
## siRNAsiPARP1                                    2.16e-06 ***
## genotypeALC1KO                                  5.20e-07 ***
## poly(Bleomycin, 2)1:siRNAsiPARP1                0.527273    
## poly(Bleomycin, 2)2:siRNAsiPARP1                0.000321 ***
## poly(Bleomycin, 2)1:genotypeALC1KO              0.357198    
## poly(Bleomycin, 2)2:genotypeALC1KO              0.000103 ***
## siRNAsiPARP1:genotypeALC1KO                     6.99e-05 ***
## poly(Bleomycin, 2)1:siRNAsiPARP1:genotypeALC1KO 0.642327    
## poly(Bleomycin, 2)2:siRNAsiPARP1:genotypeALC1KO 0.001859 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.074 on 36 degrees of freedom
## Multiple R-squared:  0.9704, Adjusted R-squared:  0.9614 
## F-statistic: 107.4 on 11 and 36 DF,  p-value: < 2.2e-16

cat("AIC: ", AIC(fit7))

## AIC:  -101.5405

simres <- simulateResiduals(fittedModel = fit7)
plot(simres)

fit8 <- lmer(Counts ~ poly(Bleomycin,2)*siRNA*genotype + (1|UID), data = dataset)
print(summary(fit8))

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Counts ~ poly(Bleomycin, 2) * siRNA * genotype + (1 | UID)
##    Data: dataset
## 
## REML criterion at convergence: 554.3
## 
## Scaled residuals: 
##      Min       1Q   Median       3Q      Max 
## -2.40400 -0.26632 -0.08172  0.47563  1.71356 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  UID      (Intercept) 175352   418.8   
##  Residual             212556   461.0   
## Number of obs: 48, groups:  UID, 12
## 
## Fixed effects:
##                                                 Estimate Std. Error      df
## (Intercept)                                        762.1      276.0     8.0
## poly(Bleomycin, 2)1                              -3001.1      922.1    28.0
## poly(Bleomycin, 2)2                                180.7      922.1    28.0
## siRNAsiPARP1                                      -343.8      390.3     8.0
## genotypeALC1KO                                      54.0      390.3     8.0
## poly(Bleomycin, 2)1:siRNAsiPARP1                   269.4     1304.0    28.0
## poly(Bleomycin, 2)2:siRNAsiPARP1                  1012.1     1304.0    28.0
## poly(Bleomycin, 2)1:genotypeALC1KO               -2191.6     1304.0    28.0
## poly(Bleomycin, 2)2:genotypeALC1KO                1616.6     1304.0    28.0
## siRNAsiPARP1:genotypeALC1KO                         76.5      552.0     8.0
## poly(Bleomycin, 2)1:siRNAsiPARP1:genotypeALC1KO   1592.0     1844.2    28.0
## poly(Bleomycin, 2)2:siRNAsiPARP1:genotypeALC1KO  -1661.6     1844.2    28.0
##                                                 t value Pr(>|t|)   
## (Intercept)                                       2.761  0.02462 * 
## poly(Bleomycin, 2)1                              -3.255  0.00296 **
## poly(Bleomycin, 2)2                               0.196  0.84604   
## siRNAsiPARP1                                     -0.881  0.40413   
## genotypeALC1KO                                    0.138  0.89338   
## poly(Bleomycin, 2)1:siRNAsiPARP1                  0.207  0.83783   
## poly(Bleomycin, 2)2:siRNAsiPARP1                  0.776  0.44417   
## poly(Bleomycin, 2)1:genotypeALC1KO               -1.681  0.10396   
## poly(Bleomycin, 2)2:genotypeALC1KO                1.240  0.22538   
## siRNAsiPARP1:genotypeALC1KO                       0.139  0.89319   
## poly(Bleomycin, 2)1:siRNAsiPARP1:genotypeALC1KO   0.863  0.39533   
## poly(Bleomycin, 2)2:siRNAsiPARP1:genotypeALC1KO  -0.901  0.37526   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Correlation of Fixed Effects:
##                   (Intr) pl(B,2)1 pl(B,2)2 sRNAsPARP1 gALC1K pl(B,2)1:RNAPARP1
## ply(Blm,2)1        0.000                                                      
## ply(Blm,2)2        0.000  0.000                                               
## siRNAsPARP1       -0.707  0.000    0.000                                      
## gntypALC1KO       -0.707  0.000    0.000    0.500                             
## pl(B,2)1:RNAPARP1  0.000 -0.707    0.000    0.000      0.000                  
## pl(B,2)2:RNAPARP1  0.000  0.000   -0.707    0.000      0.000  0.000           
## p(B,2)1:ALC        0.000 -0.707    0.000    0.000      0.000  0.500           
## p(B,2)2:ALC        0.000  0.000   -0.707    0.000      0.000  0.000           
## sRNAPARP1:A        0.500  0.000    0.000   -0.707     -0.707  0.000           
## p(B,2)1:RNAPARP1:  0.000  0.500    0.000    0.000      0.000 -0.707           
## p(B,2)2:RNAPARP1:  0.000  0.000    0.500    0.000      0.000  0.000           
##                   pl(B,2)2:RNAPARP1 p(B,2)1:A p(B,2)2:A sRNAPARP1:
## ply(Blm,2)1                                                       
## ply(Blm,2)2                                                       
## siRNAsPARP1                                                       
## gntypALC1KO                                                       
## pl(B,2)1:RNAPARP1                                                 
## pl(B,2)2:RNAPARP1                                                 
## p(B,2)1:ALC        0.000                                          
## p(B,2)2:ALC        0.500             0.000                        
## sRNAPARP1:A        0.000             0.000     0.000              
## p(B,2)1:RNAPARP1:  0.000            -0.707     0.000     0.000    
## p(B,2)2:RNAPARP1: -0.707             0.000    -0.707     0.000    
##                   p(B,2)1:RNAPARP1:
## ply(Blm,2)1                        
## ply(Blm,2)2                        
## siRNAsPARP1                        
## gntypALC1KO                        
## pl(B,2)1:RNAPARP1                  
## pl(B,2)2:RNAPARP1                  
## p(B,2)1:ALC                        
## p(B,2)2:ALC                        
## sRNAPARP1:A                        
## p(B,2)1:RNAPARP1:                  
## p(B,2)2:RNAPARP1:  0.000

cat("AIC: ", AIC(fit8))

## AIC:  582.2921

simres <- simulateResiduals(fittedModel = fit8)
plot(simres)

Cubic formula

fit9 <- lm(Counts ~ Experiment + poly(Bleomycin,3)*siRNA*genotype, data = dataset)
print(summary(fit9))

## 
## Call:
## lm(formula = Counts ~ Experiment + poly(Bleomycin, 3) * siRNA * 
##     genotype, data = dataset)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -1085.48  -228.62   -45.29   223.66   963.77 
## 
## Coefficients:
##                                                 Estimate Std. Error t value
## (Intercept)                                       262.56     169.01   1.554
## Experimentexp2                                    660.81     169.01   3.910
## Experimentexp3                                    837.75     169.01   4.957
## poly(Bleomycin, 3)1                             -3001.14     956.05  -3.139
## poly(Bleomycin, 3)2                               180.71     956.05   0.189
## poly(Bleomycin, 3)3                               562.81     956.05   0.589
## siRNAsiPARP1                                     -343.75     195.15  -1.761
## genotypeALC1KO                                     54.00     195.15   0.277
## poly(Bleomycin, 3)1:siRNAsiPARP1                  269.40    1352.06   0.199
## poly(Bleomycin, 3)2:siRNAsiPARP1                 1012.10    1352.06   0.749
## poly(Bleomycin, 3)3:siRNAsiPARP1                 -865.23    1352.06  -0.640
## poly(Bleomycin, 3)1:genotypeALC1KO              -2191.58    1352.06  -1.621
## poly(Bleomycin, 3)2:genotypeALC1KO               1616.58    1352.06   1.196
## poly(Bleomycin, 3)3:genotypeALC1KO                -51.65    1352.06  -0.038
## siRNAsiPARP1:genotypeALC1KO                        76.50     275.99   0.277
## poly(Bleomycin, 3)1:siRNAsiPARP1:genotypeALC1KO  1591.97    1912.10   0.833
## poly(Bleomycin, 3)2:siRNAsiPARP1:genotypeALC1KO -1661.61    1912.10  -0.869
## poly(Bleomycin, 3)3:siRNAsiPARP1:genotypeALC1KO   272.12    1912.10   0.142
##                                                 Pr(>|t|)    
## (Intercept)                                     0.130778    
## Experimentexp2                                  0.000489 ***
## Experimentexp3                                  2.63e-05 ***
## poly(Bleomycin, 3)1                             0.003787 ** 
## poly(Bleomycin, 3)2                             0.851352    
## poly(Bleomycin, 3)3                             0.560482    
## siRNAsiPARP1                                    0.088354 .  
## genotypeALC1KO                                  0.783903    
## poly(Bleomycin, 3)1:siRNAsiPARP1                0.843413    
## poly(Bleomycin, 3)2:siRNAsiPARP1                0.459953    
## poly(Bleomycin, 3)3:siRNAsiPARP1                0.527071    
## poly(Bleomycin, 3)1:genotypeALC1KO              0.115500    
## poly(Bleomycin, 3)2:genotypeALC1KO              0.241202    
## poly(Bleomycin, 3)3:genotypeALC1KO              0.969779    
## siRNAsiPARP1:genotypeALC1KO                     0.783538    
## poly(Bleomycin, 3)1:siRNAsiPARP1:genotypeALC1KO 0.411663    
## poly(Bleomycin, 3)2:siRNAsiPARP1:genotypeALC1KO 0.391747    
## poly(Bleomycin, 3)3:siRNAsiPARP1:genotypeALC1KO 0.887783    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 478 on 30 degrees of freedom
## Multiple R-squared:  0.7687, Adjusted R-squared:  0.6376 
## F-statistic: 5.864 on 17 and 30 DF,  p-value: 1.367e-05

cat("AIC: ", AIC(fit9))

## AIC:  743.9456

simres <- simulateResiduals(fittedModel = fit9)
plot(simres)

fit10 <- lm(NormCounts ~ poly(Bleomycin,3)*siRNA*genotype, data = dataset)
print(summary(fit10))

## 
## Call:
## lm(formula = NormCounts ~ poly(Bleomycin, 3) * siRNA * genotype, 
##     data = dataset)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.42204 -0.04856 -0.01251  0.06747  0.55506 
## 
## Coefficients:
##                                                   Estimate Std. Error t value
## (Intercept)                                      1.000e+00  5.157e-02  19.391
## poly(Bleomycin, 3)1                             -4.033e+00  3.573e-01 -11.288
## poly(Bleomycin, 3)2                              2.759e-01  3.573e-01   0.772
## poly(Bleomycin, 3)3                              6.525e-01  3.573e-01   1.826
## siRNAsiPARP1                                    -1.984e-16  7.293e-02   0.000
## genotypeALC1KO                                  -4.092e-16  7.293e-02   0.000
## poly(Bleomycin, 3)1:siRNAsiPARP1                -2.174e+00  5.053e-01  -4.303
## poly(Bleomycin, 3)2:siRNAsiPARP1                 2.333e+00  5.053e-01   4.617
## poly(Bleomycin, 3)3:siRNAsiPARP1                -1.425e+00  5.053e-01  -2.821
## poly(Bleomycin, 3)1:genotypeALC1KO              -2.582e+00  5.053e-01  -5.111
## poly(Bleomycin, 3)2:genotypeALC1KO               2.655e+00  5.053e-01   5.254
## poly(Bleomycin, 3)3:genotypeALC1KO              -6.802e-01  5.053e-01  -1.346
## siRNAsiPARP1:genotypeALC1KO                      2.417e-16  1.031e-01   0.000
## poly(Bleomycin, 3)1:siRNAsiPARP1:genotypeALC1KO  2.562e+00  7.146e-01   3.585
## poly(Bleomycin, 3)2:siRNAsiPARP1:genotypeALC1KO -2.919e+00  7.146e-01  -4.084
## poly(Bleomycin, 3)3:siRNAsiPARP1:genotypeALC1KO  1.243e+00  7.146e-01   1.740
##                                                 Pr(>|t|)    
## (Intercept)                                      < 2e-16 ***
## poly(Bleomycin, 3)1                             1.08e-12 ***
## poly(Bleomycin, 3)2                             0.445621    
## poly(Bleomycin, 3)3                             0.077154 .  
## siRNAsiPARP1                                    1.000000    
## genotypeALC1KO                                  1.000000    
## poly(Bleomycin, 3)1:siRNAsiPARP1                0.000149 ***
## poly(Bleomycin, 3)2:siRNAsiPARP1                6.03e-05 ***
## poly(Bleomycin, 3)3:siRNAsiPARP1                0.008160 ** 
## poly(Bleomycin, 3)1:genotypeALC1KO              1.44e-05 ***
## poly(Bleomycin, 3)2:genotypeALC1KO              9.50e-06 ***
## poly(Bleomycin, 3)3:genotypeALC1KO              0.187700    
## siRNAsiPARP1:genotypeALC1KO                     1.000000    
## poly(Bleomycin, 3)1:siRNAsiPARP1:genotypeALC1KO 0.001104 ** 
## poly(Bleomycin, 3)2:siRNAsiPARP1:genotypeALC1KO 0.000276 ***
## poly(Bleomycin, 3)3:siRNAsiPARP1:genotypeALC1KO 0.091497 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.1786 on 32 degrees of freedom
## Multiple R-squared:  0.975,  Adjusted R-squared:  0.9633 
## F-statistic: 83.23 on 15 and 32 DF,  p-value: < 2.2e-16

cat("AIC: ", AIC(fit10))

## AIC:  -14.59004

simres <- simulateResiduals(fittedModel = fit10)
plot(simres)

fit11 <- lm(NormCounts2 ~ poly(Bleomycin,3)*siRNA*genotype, data = dataset)
print(summary(fit11))

## 
## Call:
## lm(formula = NormCounts2 ~ poly(Bleomycin, 3) * siRNA * genotype, 
##     data = dataset)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.15326 -0.01990 -0.00463  0.03012  0.20157 
## 
## Coefficients:
##                                                 Estimate Std. Error t value
## (Intercept)                                      0.54728    0.01926  28.421
## poly(Bleomycin, 3)1                             -2.20725    0.13341 -16.545
## poly(Bleomycin, 3)2                              0.15101    0.13341   1.132
## poly(Bleomycin, 3)3                              0.35710    0.13341   2.677
## siRNAsiPARP1                                    -0.17015    0.02723  -6.248
## genotypeALC1KO                                  -0.18412    0.02723  -6.761
## poly(Bleomycin, 3)1:siRNAsiPARP1                -0.13362    0.18867  -0.708
## poly(Bleomycin, 3)2:siRNAsiPARP1                 0.83278    0.18867   4.414
## poly(Bleomycin, 3)3:siRNAsiPARP1                -0.64854    0.18867  -3.437
## poly(Bleomycin, 3)1:genotypeALC1KO              -0.19522    0.18867  -1.035
## poly(Bleomycin, 3)2:genotypeALC1KO               0.91321    0.18867   4.840
## poly(Bleomycin, 3)3:genotypeALC1KO              -0.36716    0.18867  -1.946
## siRNAsiPARP1:genotypeALC1KO                      0.19198    0.03851   4.985
## poly(Bleomycin, 3)1:siRNAsiPARP1:genotypeALC1KO  0.13865    0.26682   0.520
## poly(Bleomycin, 3)2:siRNAsiPARP1:genotypeALC1KO -0.99441    0.26682  -3.727
## poly(Bleomycin, 3)3:siRNAsiPARP1:genotypeALC1KO  0.57786    0.26682   2.166
##                                                 Pr(>|t|)    
## (Intercept)                                      < 2e-16 ***
## poly(Bleomycin, 3)1                              < 2e-16 ***
## poly(Bleomycin, 3)2                             0.266081    
## poly(Bleomycin, 3)3                             0.011626 *  
## siRNAsiPARP1                                    5.30e-07 ***
## genotypeALC1KO                                  1.22e-07 ***
## poly(Bleomycin, 3)1:siRNAsiPARP1                0.483934    
## poly(Bleomycin, 3)2:siRNAsiPARP1                0.000108 ***
## poly(Bleomycin, 3)3:siRNAsiPARP1                0.001648 ** 
## poly(Bleomycin, 3)1:genotypeALC1KO              0.308549    
## poly(Bleomycin, 3)2:genotypeALC1KO              3.16e-05 ***
## poly(Bleomycin, 3)3:genotypeALC1KO              0.060472 .  
## siRNAsiPARP1:genotypeALC1KO                     2.08e-05 ***
## poly(Bleomycin, 3)1:siRNAsiPARP1:genotypeALC1KO 0.606887    
## poly(Bleomycin, 3)2:siRNAsiPARP1:genotypeALC1KO 0.000749 ***
## poly(Bleomycin, 3)3:siRNAsiPARP1:genotypeALC1KO 0.037891 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.0667 on 32 degrees of freedom
## Multiple R-squared:  0.9787, Adjusted R-squared:  0.9686 
## F-statistic: 97.79 on 15 and 32 DF,  p-value: < 2.2e-16

cat("AIC: ", AIC(fit11))

## AIC:  -109.1627

simres <- simulateResiduals(fittedModel = fit11)
plot(simres)

fit12 <- lmer(Counts ~ poly(Bleomycin,3)*siRNA*genotype + (1|UID), data = dataset)
print(summary(fit12))

## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Counts ~ poly(Bleomycin, 3) * siRNA * genotype + (1 | UID)
##    Data: dataset
## 
## REML criterion at convergence: 491.3
## 
## Scaled residuals: 
##      Min       1Q   Median       3Q      Max 
## -2.26168 -0.23317  0.02957  0.46884  1.68726 
## 
## Random effects:
##  Groups   Name        Variance Std.Dev.
##  UID      (Intercept) 168256   410.2   
##  Residual             240938   490.9   
## Number of obs: 48, groups:  UID, 12
## 
## Fixed effects:
##                                                 Estimate Std. Error       df
## (Intercept)                                       762.08     275.98     8.00
## poly(Bleomycin, 3)1                             -3001.14     981.71    24.00
## poly(Bleomycin, 3)2                               180.71     981.71    24.00
## poly(Bleomycin, 3)3                               562.81     981.71    24.00
## siRNAsiPARP1                                     -343.75     390.29     8.00
## genotypeALC1KO                                     54.00     390.29     8.00
## poly(Bleomycin, 3)1:siRNAsiPARP1                  269.39    1388.35    24.00
## poly(Bleomycin, 3)2:siRNAsiPARP1                 1012.10    1388.35    24.00
## poly(Bleomycin, 3)3:siRNAsiPARP1                 -865.23    1388.35    24.00
## poly(Bleomycin, 3)1:genotypeALC1KO              -2191.58    1388.35    24.00
## poly(Bleomycin, 3)2:genotypeALC1KO               1616.58    1388.35    24.00
## poly(Bleomycin, 3)3:genotypeALC1KO                -51.65    1388.35    24.00
## siRNAsiPARP1:genotypeALC1KO                        76.50     551.96     8.00
## poly(Bleomycin, 3)1:siRNAsiPARP1:genotypeALC1KO  1591.97    1963.42    24.00
## poly(Bleomycin, 3)2:siRNAsiPARP1:genotypeALC1KO -1661.61    1963.42    24.00
## poly(Bleomycin, 3)3:siRNAsiPARP1:genotypeALC1KO   272.12    1963.42    24.00
##                                                 t value Pr(>|t|)   
## (Intercept)                                       2.761  0.02462 * 
## poly(Bleomycin, 3)1                              -3.057  0.00542 **
## poly(Bleomycin, 3)2                               0.184  0.85550   
## poly(Bleomycin, 3)3                               0.573  0.57178   
## siRNAsiPARP1                                     -0.881  0.40413   
## genotypeALC1KO                                    0.138  0.89338   
## poly(Bleomycin, 3)1:siRNAsiPARP1                  0.194  0.84778   
## poly(Bleomycin, 3)2:siRNAsiPARP1                  0.729  0.47306   
## poly(Bleomycin, 3)3:siRNAsiPARP1                 -0.623  0.53902   
## poly(Bleomycin, 3)1:genotypeALC1KO               -1.579  0.12753   
## poly(Bleomycin, 3)2:genotypeALC1KO                1.164  0.25571   
## poly(Bleomycin, 3)3:genotypeALC1KO               -0.037  0.97063   
## siRNAsiPARP1:genotypeALC1KO                       0.139  0.89319   
## poly(Bleomycin, 3)1:siRNAsiPARP1:genotypeALC1KO   0.811  0.42544   
## poly(Bleomycin, 3)2:siRNAsiPARP1:genotypeALC1KO  -0.846  0.40575   
## poly(Bleomycin, 3)3:siRNAsiPARP1:genotypeALC1KO   0.139  0.89093   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

cat("AIC: ", AIC(fit12))

## AIC:  527.3356

simres <- simulateResiduals(fittedModel = fit12)
plot(simres)

Compare Results

ICtab(fit1,fit2,fit3,fit4,
      fit5,fit6,fit7,fit8,
      fit9,fit10,fit11,fit12,
      base=T)

##       AIC    dAIC   df
## fit11 -109.2    0.0 17
## fit7  -101.5    7.6 13
## fit3   -35.0   74.2 9 
## fit10  -14.6   94.6 17
## fit6   -11.4   97.7 13
## fit2    58.5  167.6 9 
## fit12  527.3  636.5 18
## fit8   582.3  691.5 14
## fit4   654.7  763.9 10
## fit5   737.1  846.3 15
## fit1   738.4  847.6 11
## fit9   743.9  853.1 19

Final Result

fit <- fit7

output <- coef(summary(fit))
output <- output[grep("Bleomycin", rownames(output)),]


rownames(output) <- gsub("poly\\(|, [1-3]\\)","", rownames(output) )
rownames(output) <- gsub("genotype",  paste0(" ",levels(dataset$genotype)[1], " vs. "), rownames(output))
rownames(output)[!(grepl("vs", rownames(output)))] <- paste(rownames(output)[!(grepl("vs", rownames(output)))], levels(dataset$genotype)[1],  sep = " in " )

rownames(output) <- gsub("siRNA",  paste0(" ",levels(dataset$siRNA)[1], " vs. "), rownames(output))
rownames(output)[!(grepl("vs.*vs| in ", rownames(output)))] <- paste(rownames(output)[!(grepl("vs.*vs| in ", rownames(output)))], levels(dataset$siRNA)[1],  sep = " in " )

rownames(output)[!(grepl("vs", rownames(output)))] <- paste(rownames(output)[!(grepl("vs", rownames(output)))], levels(dataset$siRNA)[1],  sep = " " )


# suggested result table
kable(output, row.names = T)

	Estimate	Std. Error	t value	Pr(>|t|)
Bleomycin1 in WT siCtrl	-2.2072495	0.1480067	-14.9131713	0.0000000
Bleomycin2 in WT siCtrl	0.1510077	0.1480067	1.0202760	0.3144054
Bleomycin1: siCtrl vs. siPARP1 in WT	-0.1336194	0.2093131	-0.6383711	0.5272726
Bleomycin2: siCtrl vs. siPARP1 in WT	0.8327816	0.2093131	3.9786406	0.0003209
Bleomycin1: WT vs. ALC1KO in siCtrl	-0.1952223	0.2093131	-0.9326808	0.3571983
Bleomycin2: WT vs. ALC1KO in siCtrl	0.9132115	0.2093131	4.3628969	0.0001032
Bleomycin1: siCtrl vs. siPARP1: WT vs. ALC1KO	0.1386508	0.2960134	0.4683935	0.6423270
Bleomycin2: siCtrl vs. siPARP1: WT vs. ALC1KO	-0.9944078	0.2960134	-3.3593333	0.0018587

write.table(output, file = "FigureS3_bleo_Stats_Ref_WT.txt", quote = F, sep = "\t", row.names = T, col.names = NA)

# re-fit with ALC1KO reference
dataset$genotype <- relevel(dataset$genotype, ref = "ALC1KO")

fit <- lm(NormCounts2 ~ poly(Bleomycin,2)*siRNA*genotype, data = dataset)

output <- coef(summary(fit))
output <- output[grep("Bleomycin", rownames(output)),]


rownames(output) <- gsub("poly\\(|, [1-3]\\)","", rownames(output) )
rownames(output) <- gsub("genotype",  paste0(" ",levels(dataset$genotype)[1], " vs. "), rownames(output))
rownames(output)[!(grepl("vs", rownames(output)))] <- paste(rownames(output)[!(grepl("vs", rownames(output)))], levels(dataset$genotype)[1],  sep = " in " )

rownames(output) <- gsub("siRNA",  paste0(" ",levels(dataset$siRNA)[1], " vs. "), rownames(output))
rownames(output)[!(grepl("vs.*vs| in ", rownames(output)))] <- paste(rownames(output)[!(grepl("vs.*vs| in ", rownames(output)))], levels(dataset$siRNA)[1],  sep = " in " )

rownames(output)[!(grepl("vs", rownames(output)))] <- paste(rownames(output)[!(grepl("vs", rownames(output)))], levels(dataset$siRNA)[1],  sep = " " )


# suggested result table
kable(output, row.names = T)

	Estimate	Std. Error	t value	Pr(>|t|)
Bleomycin1 in ALC1KO siCtrl	-2.4024719	0.1480067	-16.2321812	0.0000000
Bleomycin2 in ALC1KO siCtrl	1.0642192	0.1480067	7.1903440	0.0000000
Bleomycin1: siCtrl vs. siPARP1 in ALC1KO	0.0050313	0.2093131	0.0240374	0.9809555
Bleomycin2: siCtrl vs. siPARP1 in ALC1KO	-0.1616262	0.2093131	-0.7721741	0.4450512
Bleomycin1: ALC1KO vs. WT in siCtrl	0.1952223	0.2093131	0.9326808	0.3571983
Bleomycin2: ALC1KO vs. WT in siCtrl	-0.9132115	0.2093131	-4.3628969	0.0001032
Bleomycin1: siCtrl vs. siPARP1: ALC1KO vs. WT	-0.1386508	0.2960134	-0.4683935	0.6423270
Bleomycin2: siCtrl vs. siPARP1: ALC1KO vs. WT	0.9944078	0.2960134	3.3593333	0.0018587

write.table(output, file = "FigureS3_bleo_Stats_Ref_ALC1KO.txt", quote = F, sep = "\t", row.names = T, col.names = NA)

Anova

fit7a <- lm(NormCounts2 ~ poly(Bleomycin,2)*siRNA*genotype, data = dataset)
fit7b <- lm(NormCounts2 ~ poly(Bleomycin,2)*siRNA+genotype, data = dataset)

# anova table
anova(fit7a, fit7b)

## Analysis of Variance Table
## 
## Model 1: NormCounts2 ~ poly(Bleomycin, 2) * siRNA * genotype
## Model 2: NormCounts2 ~ poly(Bleomycin, 2) * siRNA + genotype
##   Res.Df     RSS Df Sum of Sq      F    Pr(>F)    
## 1     36 0.19715                                  
## 2     41 0.41795 -5   -0.2208 8.0635 3.497e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

fit7c <- lm(NormCounts2 ~ poly(Bleomycin,2)*genotype*siRNA, data = dataset)
fit7d <- lm(NormCounts2 ~ poly(Bleomycin,2)*genotype+siRNA, data = dataset)

# anova table
anova(fit7c, fit7d)

## Analysis of Variance Table
## 
## Model 1: NormCounts2 ~ poly(Bleomycin, 2) * genotype * siRNA
## Model 2: NormCounts2 ~ poly(Bleomycin, 2) * genotype + siRNA
##   Res.Df     RSS Df Sum of Sq      F    Pr(>F)    
## 1     36 0.19715                                  
## 2     41 0.39991 -5  -0.20276 7.4046 7.319e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1